Prospective media content generation using neural network modeling

ABSTRACT

A system for prospectively identifying media characteristics for inclusion in media content is disclosed. A neural network database including media characteristic information and feature information may associate relationships among the media characteristic information and feature information. Personal characteristic information associated with target media consumers may be used to select a subset of the neural network database. A first set of nodes, representing selected feature information, may be activated. The node interactions may be calculated to detect the activation of a second set of nodes, the second set of nodes representing media characteristic information. Generally, a node is activated when an activation value of the node exceeds a threshold value. Media characteristic information may be identified for inclusion in media content based on the second set of nodes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/446,741, filed Mar. 1, 2017, which is a continuation of U.S. patentapplication Ser. No. 14/644,092, filed Mar. 10, 2015, which is acontinuation of U.S. patent application Ser. No. 13/609,141, filed Sep.10, 2012, and issued as U.S. Pat. No. 8,983,885 on Mar. 17, 2015, thedisclosures of which are herein incorporated by reference in theirentirety.

BACKGROUND 1. Field

The present disclosure relates generally to the field of media contentgeneration and, more particularly, to prospective media contentgeneration using neural network modeling to encourage a behavior.

2. Related Art

Conventional media campaigns use media content to encourage certainbehaviors in the perceiving audience. Typically, a focus group isassembled to view the media content. Individuals, or the focus group asa whole, may be tracked following the media viewing to analyze changesin behavior or perception. This information may be used to determine theefficacy of the media content with respect to encouraging the behavior.Often, when the media does not sufficiently encourage the behavior, thecontent of the media is replaced or revised and the process is repeated.This may be time consuming and expensive, often resulting inunsuccessful media campaigns.

BRIEF SUMMARY

Systems and processes for selecting media characteristics for inclusionin a media content are described. A neural network database comprising aplurality of media characteristic information and a plurality of featureinformation is accessed. The neural network database associatesrelationships among the plurality of media characteristic informationand the plurality of feature information. One or more personalcharacteristic information associated with one or more target mediaconsumers is received. A subset of the neural network database isselected based on the received one or more personal characteristicinformation. A selection of one or more feature information of theplurality of feature information is received. A first set of one or morenodes of the subset of the neural network database is activated. Thefirst set of one or more nodes represents the one or more featureinformation. Node interactions are calculated based on the subset of theneural network database. The activation of a second set of one or morenodes of the subset of the neural network database is detected. Theactivation of the second set of one or more nodes of the subset of theneural network database is in response to the node interactionscalculations. The second set of one or more nodes represents one or moremedia characteristic information of the plurality of mediacharacteristic information and a node of the subset of the neuralnetwork database is activated when an activation value of the nodeexceeds a threshold value. At least one media characteristic informationis identified for inclusion in the media content. The at least one mediacharacteristic information is represented by at least one node of thesecond set of one or more nodes.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary process for prospectively generatingmedia content.

FIG. 2 illustrates an exemplary process for building a neural network.

FIG. 3 illustrates an exemplary neural network.

FIG. 4 illustrates an exemplary process for developing and refining aneural network.

FIG. 5 illustrates an exemplary computing system.

DETAILED DESCRIPTION

The following description is presented to enable a person of ordinaryskill in the art to make and use the various embodiments. Descriptionsof specific devices, techniques, and applications are provided only asexamples. Various modifications to the examples described herein will bereadily apparent to those of ordinary skill in the art, and the generalprinciples defined herein may be applied to other examples andapplications without departing from the spirit and scope of the variousembodiments. Thus, the various embodiments are not intended to belimited to the examples described herein and shown, but are to beaccorded the broadest scope consistent with the claims.

The embodiments described herein include technologies directed toprospective media content generation using neural network modeling toencourage a particular behavior. A neural network database is developedthat associates various characteristics of media with featureinformation. A subset of the neural network may be selected based onpersonal characteristic information associated with a target audience.For example, the neural network may be limited to females between aparticular age range. This may allow the neural network to be tailoredto that particular demographic. The system may activate nodes based on aselection of feature information, where the activated nodes representthe selected feature information. For example, feature information mayinclude information about attributes of media content, attributes ofpeople, attributes of products, attributes of companies, and attributesof brands. Activating these nodes may cause other nodes in the subset ofthe neural network to be activated or deactivated through calculatednode interactions. A second set of nodes that has remained activated orhas become activated is identified. The second set of nodes mayrepresent various characteristics of media, which are identified forinclusion in the media content. Media content is created using thesevarious characteristics of media in order to encourage the particularbehavior. After the targeted audience views the media content, they maybe motivated to take part in the particular behavior that wasencouraged.

FIG. 1 illustrates an exemplary process for prospectively generatingmedia content. In block 100, a neural network is developed. In oneexample, the neural network may be stored in a database. The databasemay be used to quantify positive and negative associations amongfeatures and media characteristics. For example, features may includeattributes of media content, attributes of people, attributes ofproducts, attributes of companies, and attributes of brands.

The attributes of media content may be referred to as mediacharacteristics. Thus, media characteristics may be a subset offeatures. Media characteristics may include attributes of music, visiblecolors, attributes of actors, attributes of characters portrayed, aportrayed activity, landscapes, and the like of media content. Forexample, actors may include individuals portrayed in media content. Foranother example, a one-minute film may have classical piano backgroundmusic, be predominantly composed of red colors (such as a red piano, redwalls or flooring, etc.), include only male actors taller than a certainheight, and be set indoors at a residential building.

A portrayed activity may include a portrayal of sex and a portrayal ofviolence. Attributes of music may include whether there is backgroundmusic, the genre of the music, the relative volume of the music, theinstrumentation of the music, the attributes of the vocals of thebackground music, and the like. Visible colors may include the colors ofwardrobe, props, backgrounds, scenery, translucent and opaque visualoverlays, and the like. Attributes of the actors may include the actors'physical appearance, nationality, race, gender, affective display,height, hair color, eye color, clothing, and the like. Attributes of thecharacters portrayed may include the character's nationality, ethnicity,and the like. Affective display may be an individual's externallydisplayed affect by, for example, facial, vocal, and gestural means.Affective display may include displays of anger, aggression, strength,laughter, and hatred. Landscapes may include urban settings (such as acity or town), rural settings (such as a countryside or village),mountainous regions, desert regions, outer space, and the like.

Attributes of media content may also include use of different narrativemodes, imagery, audio, and the like. Narrative modes may includenarrative point of view, such as a first-person perspective, athird-person omniscient perspective, a third-person limited perspective(e.g., overhearing an interaction between two characters), andalternating person view. Narrative modes may also include variousnarrative voices and various narrative times. Imagery may includenational symbols, monuments, and the like. Audio may include theattributes of music, such as whether the music is an instrumental or avocal, the tempo of the music, the relationship between the viewer'sculture and the cultural origin of the music, the volume of the music,the audible instruments in the music, and the like. Audio may alsoinclude voice audio and other non-music and non-voice audio.

Attributes of people may include experiences, ethnicity, nationality,gender, sexual orientation, physical characteristics, cultural identity,organizational involvement, social status, socioeconomic status,political orientation, dress preference (e.g., goth, preppy, etc.), whatmedia content they have already consumed, and the like. Attributes of aproduct may include the product's size, shape, color, composition, cost,and the like. For example, a vehicle's attributes may include thevehicle's color, brand, styling, number of doors, gas consumption,environmental friendliness, and the like. Attributes of a company mayinclude the company's size, revenue, logo design attributes, slogans,marketing policies, and the like. Attributes of a brand may include thebrand's logo, trademark, motto, tagline, and brand identity elements.

Features, and thus media characteristics, may be associated with one ormore feature types: search features, experience features, and socialfeatures. Search features may include features that are readilyperceptible through one of the senses—hearing, sight, touch, smell, andtaste. For example, search features may include the color, size, or odorof an item. Experience features may include features that requireinteraction to determine. Social features may include features that arebased on social norms, interactions, and personal experience.

In block 102, one or more desired psychological responses to bestimulated are received. In one example, the desired psychologicalresponses to be stimulated may include one or more features to be evokedin the perceiving audience. The one or more features are represented inthe neural network. The desired psychological responses to be stimulatedmay also include specific emotional and social responses, particularbehaviors, and directed changes in beliefs and biases. The desiredpsychological responses to be stimulated may be based on the goals of anassociated media campaign.

In block 104, a target perceptual profile is determined. A perceptualprofile may be an individual-level neural network or a group-levelneural network determined based on personal characteristics of a targetaudience. For example, the target audience may be based on ademographic, one or more affinities, or preferences that are selectedbased on the goals of an associated media campaign.

The target perceptual profile may be based on the neural network and thepersonal characteristics of the target audience. For example, for atarget audience that includes individuals with a specified personalcharacteristic of being female, the neural network may be modified toinclude only associations among features and media characteristics basedon data from females. Thus, a modified neural network may represent theperceptual profile of individuals with a specified personalcharacteristic. In this example, the modified neural network maygenerally represent the perceptual profile of females.

In another example, for a target audience that includes individuals witha specified personal characteristic of being female, the neural networkmay be modified to include only associations among features and mediacharacteristics based on data from individuals with a personalcharacteristic other than being female. For example, the perceptualprofile may be determined based on the feature of wearing lipstick. Thismay produce valuable results because the personal characteristic ofwearing lipstick may be closely associated with the personalcharacteristic of being female.

In one example, a perceptual profile of a target audience may beselected based on the expected or actual likes and dislikes of thetarget audience. This may be based on the expectation that individualswith similar likes and dislikes will tend to have similar perceptualprofiles. In another example, a perceptual profile of a target audiencemay be selected based on expected or actual social relationships. Thismay be based on the expectation that individuals who are sociallyrelated will tend to have similar perceptual profiles. Accordingly, asingle perceptual profile may be determined to represent the targetaudience, which may include multiple individuals.

In block 106, media characteristics that may stimulate the desiredpsychological responses are determined. This may be achieved, forexample, by using a perceptual profile that aligns with the targetaudience of the media campaign. For example, media characteristics thatwill stimulate specific emotional, cognitive, and identificationresponses may be determined based on the target perceptual profile andone or more desired psychological responses to be stimulated. The mediacharacteristics may be determined by using the desired psychologicalresponses as an input into the neural network.

In block 108, media content is produced using the determined mediacharacteristics. In one example, multiple variations of the mediacontent may be produced using a varying number or degree of thedetermined media characteristics. For example, one produced mediacontent may include only a subset of the determined mediacharacteristics, while another produced media content may include allthe determined media characteristics.

FIG. 2 illustrates an exemplary process for building a neural network.In block 200, test subjects are selected for analysis. The test subjectsmay be selected based on their personal characteristics, or they may beselected without regard to their personal characteristics. For example,if a determination is made that the neural network contains insufficientdata from individuals possessing a particular personal characteristic,individuals with that particular personal characteristic may be selectedfor analysis.

In block 202, various media content are shown to the test subjects. Themedia characteristics of the media content shown to the test subjectsmay be varied among the various media content. In one example, each testsubject will consume only one media content, the media content includingone or more determined media characteristics. In another example, eachtest subject may consume multiple media content, each media contentincluding one or more controlled changes in the media characteristics ofthe media content.

In block 204, data about the responses of the test subjects who consumethe media content is collected. To collect data on the responses of thetest subjects, the test subjects may be monitored before, during, and/orafter the consumption of the media content. The data collected may beused to determine the psychological responses brought about by theconsumed media content. Importantly, varying the media characteristicsof the media content may allow for determining which mediacharacteristics are responsible for causing particular psychologicalresponses.

In particular, three types of relationships may be measured. First, thedegree of correlation between media characteristics and physiologicalresponses may be measured. Second, the degree of correlation betweenmedia characteristics and behavioral responses may be measured. Third,the degree of correlation between physiological responses and behavioralresponses may be measured. Physiological responses may include heartrate, pupil dilation and constriction, rate of breathing, and the like.Behavioral responses may include choosing to perform an act, such asexercising, watching a particular television show, eating a particularfood, and the like. These relationships associated with responses may bemaps in a space different than that from features.

Response data may be determined in many ways. In one example,neurorecording and psychometric techniques may be used to capture threemajor categories of responses: emotional arousal, cognitive processing,and identification and perspective taking. One of ordinary skill in theart will readily appreciate that neurorecording and psychometrictechniques may also be used to capture other categories of responses.

Emotional arousal, also known as emotional engagement, during anexperience may enhance the recall of information associated with thatexperience. For example, individuals may recall such “emotionalinformation” better than information associated with a neutral event.Activation of certain regions of the brain associated with emotion, asmeasured by functional magnetic resonance imaging (fMRI), may becorrelated with long-term recall of emotional information and may beindicative of emotional arousal. Other measurements of emotionalengagement are also possible. For example, galvanic skin response (GSR)may be used to determine emotional arousal. Accordingly, the experiencesmay be positively, negatively, or neutrally associated with emotionalarousal in the neural network.

Cognitive processing may also be associated with psychological effectsrelevant to recall of information. For example, contemplating stressfulevents, or even traumatic events, may be associated with increasedactivity in certain areas of the brain associated with cognitiveprocessing. Such increased activity in the areas of the brain associatedwith cognitive processing may be correlated with enhanced recall ofnon-emotional information. Accordingly, experienced or perceived eventsmay be positively, negatively, or neutrally associated with stress ortrauma.

The ability to identify with characters in a narrative may also beassociated with increased recall of information and an increasedwillingness to follow the behavioral directives presented by thenarrative in the media content. For example, the ability to take a firstperson perspective when viewing media content may be integral to feelingempathy toward others. In another example, the ability to take a removedperspective may be associated with increased emotional and physicallong-term health. Accordingly, narrative perspectives may be positively,negatively, or neutrally associated with emotions and experiences in theneural network.

In addition to fMRI and GSR, various other techniques may be used todetermine the effects of media characteristics on the consumers of themedia. For example, electroencephalography (EEG) may be used to takegross measurements of cognitive processing, including hemisphericaldifferences. Electrocardiography (EKG), pulse rate, and pupil dilationmay be used to measure emotional arousal. Additionally, eye tracking maybe used to assess the viewer's involvement in the narrative, as well asprovide information regarding which elements of the media imagery arethe most salient to the viewer.

In one example, neurorecording and psychometric techniques may be usedto identify that a particular individual positively associates twofeatures: a particular country and the concept of family. For example,the individual may have traveled to that country and may haveexperienced that family members in that country are very intimate andclose. For another example, the positive association may be a result ofthe individual having immediate family members that live, or have lived,in that country.

In another example, Internet data may be used to identify positive andnegative associations between various features and mediacharacteristics. More specifically, computational linguistic methods maybe used to extract data associating various features and mediacharacteristics from social-network profiles, web pages, blogs,microblogs, product reviews, narratives, web browsing histories, and thelike. For example, text mining, statistical pattern learning, naturallanguage processing, and sentiment analysis may be used to identify theassociations between the various features and media characteristics. Theassociation information may be represented in the neural network.

In block 206, the features and media characteristics are added to theneural network. For example, the neural network may be represented, inpart, by nodes and edges and stored in a database on a computer system.Importantly, once the neural network has matured in terms of the numberof personal characteristics represented, it may be unnecessary tocontinue testing actual subjects. Instead, the neural network may beused to determine media characteristics that may stimulate desiredpsychological responses.

FIG. 3 illustrates an exemplary neural network 300. The neural network300 may be used to model the psychological changes that are broughtabout in an individual as a result of the individual's perception of afeature. Encoding features 302, 304, 306, 308 and media characteristics304, 308 into a single computational space allows the neural network 300to capture the relationships among the various features 302, 304, 306,308 and various media characteristics 304, 308. Using theserelationships, the neural network 300 formalizes the expectedpsychological responses that will result from the consumption of mediacontent including media characteristics by an individual or a group ofindividuals.

A space S={−1,0,1}^(D) may be defined where D is the number of features.The space S may include both features and media characteristics. VectorsVin this space may be interpreted as follows:

$V_{i} = \{ \begin{matrix}{1\ } & {V\mspace{14mu} {has}\mspace{14mu} {characteristic}\mspace{20mu} i} \\{{- 1}\ } & {V\ {does}\mspace{14mu} {not}\mspace{14mu} {have}\mspace{14mu} {character}\; {ist}\; {ic}\ i} \\{0\ } & {i\mspace{14mu} {is}\mspace{14mu} {not}\mspace{14mu} {salient}\mspace{14mu} {to}\mspace{9mu} V}\end{matrix} $

Thus, any node in the network may be in any one of three possiblestates, {1,−1,0}. Objects in the space S may be referred to as“patterns.” Both features and characteristics of media content may berepresented by patterns. E may be defined as the space of all sets offeatures and media characteristics. That is, E is the space of allfinite vectors of elements from S:

E ≡ ∅ ⋃ ⋃ 2 ⋃  … = ⋃ i = 0 ∞   i

An element PϵE is a set of patterns pϵS. An individual may have a set offeatures described by an element PϵE. In one example, the same patternmay appear multiple times in P. The patterns in the individual's set offeatures P affect the way the individual processes information when heor she consumes media content. Specifically, there is some functionA:E×S→S taking a feature set PϵE and some initial pattern X′ϵS toanother pattern A(P,X^(i))ϵS.

The connections 310, 312, 314, 316, 318, 320 between nodes represent thelevel of positive and negative associations between the nodes. Theseconnection weights may be numbers in [−1, 1] that represent the degreeto which two features are related in the experience of the mediaconsumer. For example, an individual may find the features of “fruit”and “health” to be very closely related. This association may bereflected by the weight between the two features, w_(fruit,health),which may be positive and relatively close to 1. In another example, anindividual may find the features of “dessert” and “health” to benegatively related. This association may be reflected by the weightbetween the two features, w_(dessert,health), which may be negative andrelatively close to −1.

In one example, the three types of measured relationships discussedabove, the degree of correlation between media characteristics andphysiological responses, the degree of correlation between mediacharacteristics and behavioral responses, and the degree of correlationbetween physiological responses and behavioral responses, may be used todetermine the weights between features. The weights between the featuresin the neural network may be based on one of the three types of measuredrelationships, or may be based on a combination of two or more types ofmeasured relationships. In another example, the weights between featuresmay be determined using computational linguistic methods. For example,text mining, statistical pattern learning, natural language processing,and sentiment analysis may be used to determine the weights betweenfeatures.

In one example, when P=Ø, w_(ij)=0. When P≠Ø, for i≠j:

$w_{ij} = {w_{j,i} = \frac{\sum_{p \in P}{p_{i}p_{j}}}{P}}$

One of ordinary skill in the art will readily appreciate that othermethods for calculating the weights may also be used.

In addition, a node may have a self-connection with a weight w_(i,j)≥0.This may represent the degree to which the feature represented by thenode is “persistent.” Persistence may be described as the magnitude ofthe input that the node sends to itself. Because w_(i,j)≥0, the inputthat the node sends to itself may have the same sign as v_(i), i.e., theinput may be congruent with the current state of the node. Thisself-connection may be defined exogenously.

The state of the neural network may be represented by a vectorcontaining the state of each node in the network, where the elements VϵSdescribe the states of the neural network. The vector of inputs to eachnode may be I=W(P)·V in

^(D). Thus, the input to node i is I_(i).

Each node in the neural network may have an activation threshold α_(i).In one example, α_(i)>0 for each node in the neural network. In anotherexample, α_(i)≥0 for each node in the neural network. For example, theactivation thresholds of nodes may be based on estimates of how commonthe feature is for a particular demographic. The activation thresholdsmay be determined by analyzing statistical information, social mediainformation, and the like of a particular demographic. Specifically, theactivation threshold of a node may be based on how salient a feature,which is represented by the node, is to the particular demographic.Thus, α_(i) may be inversely proportional to the frequency of thefeature (both negative and positive) in an experience set. For example:

$\alpha_{i} \propto \frac{P}{\sum\limits_{p \in P}{p_{i}}}$

In one example, the activation threshold of a node may be based on thethree types of measured relationships discussed above: the degree ofcorrelation between media characteristics and physiological responses,the degree of correlation between media characteristics and behavioralresponses, and the degree of correlation between physiological responsesand behavioral responses. The activation threshold of a node may bebased on one of the three types of measured relationships, or may bebased on a combination of two or more types of measured relationships.In another example, the activation thresholds of a node may bedetermined using computational linguistic methods. For example, textmining, statistical pattern learning, natural language processing, andsentiment analysis may be used to determine the activation thresholds ofthe nodes.

In one example, the activation threshold of a node associated with thefeature of “sexy” may be determined for the demographic of women byanalyzing one or more television shows with a high viewership of women.The activation threshold may be based on the fraction of characters inthe television shows being associated with the feature. When thefraction is large, the activation threshold may be small. A smalleractivation threshold may indicate that the feature is more salient tothe particular demographic. Alternatively, and less preferably, theactivation thresholds of nodes may be defined exogenously.

The higher the value of the threshold α_(i), the harder it may be toactivate the node i. Similarly, the higher the value of the thresholdα_(i), the harder it may be to associate the feature of node i with anyother set of features.

The neural network with D nodes may be characterized by the D×D matrixW(P) and the vector of activation thresholds α. The states of the nodesin the neural network may be updated based on the updated node's inputI_(i). Preferably, the nodes may be updated asynchronously, i.e., thenodes are updated one at a time. Asynchronous updating rules may be morelikely to cause the neural network to converge to a single stable state.For asynchronous updating rules, the order of node updates may berandom, or the order of node updates may be based on the attributes ofthe nodes in the neural network. Alternatively, the nodes may be updatedsynchronously, i.e., the nodes are updated simultaneously.

Each node may have a transition function T′:S→S, where:

${T_{i}^{i}(V)} = \{ {{{\begin{matrix}{{- 1},} & {I_{i} = {{\sum\limits_{j}{w_{i,j}V_{j}}} < {- \alpha_{i}}}} \\{0,} & {{- \alpha_{i}} \leq I_{i} \leq \alpha_{i}} \\{1,} & {\alpha_{i} < I_{i}}\end{matrix}{T_{j}^{i}(V)}} = V_{j}},{j \neq {i.}}} $

When T_(i) ^(j)(V)=V_(i), the node may be determined to be stable. Nodesin the network may continue to be updated until T^(i)(V)=V for all i.This may indicate that the neural network has reached a stable state.

Thus, when a node associated with a feature is activated, the node maycontribute to the activation (or deactivation) of connected nodes basedon the weighted connections between the nodes. If a connected node isactivated, it may also contribute to the activation (or deactivation) ofnodes to which it is connected. This may continue to occur until thesystem has reached equilibrium.

FIG. 4 illustrates an exemplary process for developing and refining aneural network for encouraging a certain behavior. At block 400, adatabase including a neural network describing specific effects ofnarrative techniques, imagery, and music on affective processing,cognitive processing, and perspective taking is accessed. The databasemay include quantified relationships among the various narrativetechniques, imagery, music and the affective processing, cognitiveprocessing, and perspective taking. In block 402, the database is usedto select specific media characteristics that may engage specificaffective processing, cognitive processing, and perspective taking. Theselection of the specific media characteristics is based on the desiredbehavior to be induced. In block 404, media content is produced thatincludes the selected media characteristics. In block 406, a focus groupof individuals is used to measure the effectiveness of consuming themedia content with respect to encouraging the certain behavior. This maybe done by performing neurorecording and physiological recording on thefocus group while the focus group is consuming the media content. Inblock 408, the measured effectiveness of consuming the media contentwith respect to encouraging the certain behavior is used to update thestrength of the connections among the features in the neural network.This process may be repeated in order to repeatedly refine the neuralnetwork database.

In one example, the system may receive information about a mediacharacteristic. The system may determine how the existing neural networkassociated with an intended audience will evolve when the intendedaudience perceives the media characteristic in media content. Forexample, given that an actor will be portrayed in a particular manner inmedia content, the system may determine the expected changes that mayoccur in the neural network based on the intended audience perceivingthe actor being portrayed in the particular manner. Activationthresholds and weights of the neural network may be modified based onthese expected changes.

FIG. 5 depicts an exemplary computing system 500 configured to performany one of the above-described processes. In this context, computingsystem 500 may include, for example, a processor, memory, storage, andinput/output devices (e.g., monitor, keyboard, touchscreen, disk drive,Internet connection, etc.). However, computing system 500 may includecircuitry or other specialized hardware for carrying out some or allaspects of the processes. In some operational settings, computing system500 may be configured as a system that includes one or more units, eachof which is configured to carry out some aspects of the processes eitherin software, hardware, or some combination thereof.

FIG. 5 depicts computing system 500 with a number of components that maybe used to perform the above-described processes. The main system 502includes a motherboard 504 having an input/output (“I/O”) section 506,one or more central processing units (“CPU”) 508, and a memory section510, which may have a flash memory device 512 related to it. The I/Osection 506 is connected to a display 524, a keyboard 514, a diskstorage unit 516, and a media drive unit 518. The media drive unit 518can read/write a computer-readable medium 520, which can containprograms 522 and/or data. The I/O section 506 may also connect to cloudstorage using, for example, cellular data communications or wirelesslocal area network communications.

At least some values based on the results of the above-describedprocesses can be saved for subsequent use. Additionally, anon-transitory computer-readable medium can be used to store (e.g.,tangibly embody) one or more computer programs for performing any one ofthe above-described processes by means of a computer. The computerprogram may be written, for example, in a general-purpose programminglanguage (e.g., Perl, C, C++, Java) or some specializedapplication-specific language.

Although only certain exemplary embodiments have been described indetail above, those skilled in the art will readily appreciate that manymodifications are possible in the exemplary embodiments withoutmaterially departing from the novel teachings and advantages of thisinvention. For example, aspects of embodiments disclosed above can becombined in other combinations to form additional embodiments.Accordingly, all such modifications are intended to be included withinthe scope of this invention.

What is claimed is:
 1. A computing system configured for performing aset of acts comprising: accessing a neural network database that storesweights of a neural network, wherein the weights of the neural networkare indicative of a plurality of associations between a plurality ofmedia characteristic information and a plurality of feature information;and using the neural network to select an attribute of an actor forinclusion in a media content by: receiving a selection of a feature tobe evoked in a target consumer, wherein the feature is a subset of theplurality of feature information, based on the selection, activating afirst set of one or more nodes of the neural network, the first set ofone or more nodes representing the feature, calculating nodeinteractions based on the neural network, detecting the activation of asecond set of one or more nodes of the neural network, the second set ofthe one or more nodes of the neural network being activated in responseto the calculating node interactions, and selecting the attribute of theactor based on the attribute of the actor being represented by at leastone node of the second set of one or more nodes.
 2. The computing systemof claim 1, wherein the set of acts further comprises: receivingpersonal characteristic information associated with the target consumer;and selecting a subset of the neural network based on the receivedpersonal characteristic information, wherein the subset of the neuralnetwork excludes portions of the neural network based on the excludedportions not corresponding to the personal characteristic information.3. The computing system of claim 2, wherein using the neural network toselect the attribute of the actor comprises using the subset of theneural network to select the attribute of the actor.
 4. The computingsystem of claim 2, wherein the personal characteristic is selected fromthe group consisting of age, gender, nationality, race, ethnicity,sexual orientation, socioeconomic status, and political orientation. 5.The computing system of claim 1, wherein the feature is selected fromthe group consisting of an attribute of a person, an attribute of aproduct, an attribute of a company, and an attribute of a brand.
 6. Thecomputing system of claim 1, wherein detecting the activation of thesecond set of one or more nodes of the neural network comprises:determining at least one threshold value; and comparing the at least onethreshold value to an activation value, the activation value at leastpartially calculated based on the node interactions calculation.
 7. Thecomputing system of claim 1, wherein calculating node interactions basedon the neural network comprises calculating an activation value based ona weighted sum of one or more input values received by at least one nodeof the second set of one or more nodes.
 8. A method comprising:accessing a neural network database that stores weights of a neuralnetwork, wherein the weights of the neural network are indicative of aplurality of associations between a plurality of media characteristicinformation and a plurality of feature information; and using the neuralnetwork to select an attribute of an actor for inclusion in a mediacontent by: receiving a selection of a feature to be evoked in a targetconsumer, wherein the feature is a subset of the plurality of featureinformation, based on the selection, activating a first set of one ormore nodes of the neural network, the first set of one or more nodesrepresenting the feature, calculating node interactions based on theneural network, detecting the activation of a second set of one or morenodes of the neural network, the second set of the one or more nodes ofthe neural network being activated in response to the calculating nodeinteractions, and selecting the attribute of the actor based on theattribute of the actor being represented by at least one node of thesecond set of one or more nodes.
 9. The method of claim 8, furthercomprising: receiving personal characteristic information associatedwith the target consumer; and selecting a subset of the neural networkbased on the received personal characteristic information, wherein thesubset of the neural network excludes portions of the neural networkbased on the excluded portions not corresponding to the personalcharacteristic information.
 10. The method of claim 9, wherein using theneural network to select the attribute of the actor comprises using thesubset of the neural network to select the attribute of the actor. 11.The method of claim 9, wherein the personal characteristic is selectedfrom the group consisting of age, gender, nationality, race, ethnicity,sexual orientation, socioeconomic status, and political orientation. 12.The method of claim 8, wherein the feature is selected from the groupconsisting of an attribute of a person, an attribute of a product, anattribute of a company, and an attribute of a brand.
 13. The method ofclaim 8, wherein detecting the activation of the second set of one ormore nodes of the neural network comprises: determining at least onethreshold value; and comparing the at least one threshold value to anactivation value, the activation value at least partially calculatedbased on the node interactions calculation.
 14. The method of claim 8,wherein calculating node interactions based on the neural networkcomprises calculating an activation value based on a weighted sum of oneor more input values received by at least one node of the second set ofone or more nodes.
 15. A computing system configured for performing aset of acts comprising: accessing a neural network database that storesweights of a neural network, wherein the weights of the neural networkare indicative of a plurality of associations between a plurality ofmedia characteristic information and a plurality of feature information;and using the neural network to select a portrayed activity forinclusion in a media content by: receiving a selection of a feature tobe evoked in a target consumer, wherein the feature is a subset of theplurality of feature information, based on the selection, activating afirst set of one or more nodes of the neural network, the first set ofone or more nodes representing the feature, calculating nodeinteractions based on the neural network, detecting the activation of asecond set of one or more nodes of the neural network, the second set ofthe one or more nodes of the neural network being activated in responseto the calculating node interactions, and selecting the portrayedactivity based on the portrayed activity being represented by at leastone node of the second set of one or more nodes.
 16. The computingsystem of claim 15, wherein the set of acts further comprises: receivingpersonal characteristic information associated with the target consumer;and selecting a subset of the neural network based on the receivedpersonal characteristic information, wherein the subset of the neuralnetwork excludes portions of the neural network based on the excludedportions not corresponding to the personal characteristic information.17. The computing system of claim 16, wherein using the neural networkto select the portrayed activity comprises using the subset of theneural network to select the portrayed activity.
 18. The computingsystem of claim 16, wherein the personal characteristic is selected fromthe group consisting of age, gender, nationality, race, ethnicity,sexual orientation, socioeconomic status, and political orientation. 19.The computing system of claim 15, wherein the feature is selected fromthe group consisting of an attribute of an actor, an attribute of aperson, an attribute of a product, an attribute of a company, and anattribute of a brand.
 20. The computing system of claim 15, whereindetecting the activation of the second set of one or more nodes of theneural network comprises: determining at least one threshold value; andcomparing the at least one threshold value to an activation value, theactivation value at least partially calculated based on the nodeinteractions calculation.